1. Field of the Invention
The present invention relates to a computer-based method for quantifying peaks in an analytical signal by recording successive signal values of the analytical signal and applying a peak analysis methodology to the recorded successive signal values within an interval for obtaining a set of peak quantification results. The interval may contain all signal values or the signal values of a portion of interest of the recorded analytical signal.
2. Description of the Related Art
The majority of conventional peak analysis algorithms commonly used for quantification of peaks (i.e., peak areas or peak heights) in analytical signals, such as chromatograms or spectra, are based on derivatives of the analytical signals. These derivatives of the analytical signals are inherently sensitive to noise and signal shifts, especially when low signal-to-noise ratio (S/N) signals are involved. Real peaks often have poor shapes, such as tailing, fronting, split peaks and/or shouldered peaks. Consequently, a peak analysis algorithm often requires optimization of a set of algorithm parameters to permit adaption of the algorithm to different peak shapes, which is also often sensitive to noise and shifts in a “raw” signal. Thus, the natural presence of noise and/or signal shifts in analytical signals often interferes with quantification of the peaks, which causes inaccurate, imprecise, and sometimes erroneous quantification of the analytes corresponding to these peaks.
In a laboratory environment, it may be feasible, albeit expensive or tedious, for an experienced user to manually inspect the results of peak quantification for serious errors caused by such interference. However, the consequence of this interference is often more serious in a continuous and unattended process monitoring environment, where erroneously reported results may require expensive manual inspections of the monitored process, and may result, for example, in inaccurate gas custody transfer billing, or may even cause the initiation of an erroneous emergency shutdown preparation of an entire process line.
This problem has only been partially solved by filtering out a portion of the noise prior to performing peak analysis. Conventional filtering methods merely include simple moving average, Savitzky-Golay smoothing, Gaussian filtering, Fourier transform filtering, and wavelet transform filtering.